Depth-Camera-Based In-line Evaluation of Surface Geometry and Material Classification For Robotic Spraying
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Author / Producer
Date
2020-10
Publication Type
Conference Paper
ETH Bibliography
yes
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Abstract
This paper presents a feasibility study of surface geometry (SG) evaluation and material classification (MC) for robotic spraying. We propose two complementary approaches using point clouds and intensity data provided by a state-of-the-art industrial time-of-flight (ToF) depth camera. The SG evaluation is based on geometric feature computation within local neighbourhoods, which are then used within a supervised classification. The results of this approach are SG classes according to the level of geometric variability of the surface, displayed as SG maps. For MC, active reflectance estimation is investigated and exploited to derive features related to the reflectance and diffusive properties of each material for classification. The result of both approaches can be prospectively used as feedback in digital fabrication for in-line adaptation of the process to improve control of relevant geometrical and material properties.
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Publication status
published
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Editor
Book title
2020 Proceedings of the 37th ISARC (online)
Journal / series
Volume
Pages / Article No.
693 - 702
Publisher
International Association for Automation and Robotics in Construction
Event
37th International Symposium on Automation and Robotics in Construction (ISARC 2020) (virtual)
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Surface Geometry; Material Classification; Digital Fabrication; Depth Camera; Machine Learning
Organisational unit
03964 - Wieser, Andreas / Wieser, Andreas
02284 - NFS Digitale Fabrikation / NCCR Digital Fabrication
Notes
Additional Notes": Due to the Coronavirus (COVID-19) the conference was conducted virtually.